Home Automation HVAC State Inference for Envelope Control
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Solution Overview
Problem
In home automation systems, coordinating the control of building envelope devices with heating and air conditioning systems is complex due to limited communication capabilities, leading to contradictory comfort and energy consumption criteria, and the need to know the activation state of active equipment without explicit information exchange.
Innovation Solution
A method utilizing an artificial neural network logic module to determine the operating state of the heating and air conditioning system based on temperature data, allowing for the control of home automation devices linked to the building envelope without requiring direct information from the active equipment, using temperature sensors and iterative configuration processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If home automation devices and active equipment are integrated into a unified communication system, then coordinated control and information exchange are improved, but device complexity and communication infrastructure requirements increase
Solution Approach 1:
The patent introduces an intermediary system that uses temperature sensors and neural network algorithms to mediate between active equipment and home automation devices. This intermediary infers the operational state of heating/cooling systems through temperature pattern recognition, enabling coordinated control without requiring direct communication integration between all devices.
Solution Approach 2:
The patent extracts the communication requirement from the control system by using passive temperature data collection instead of active communication protocols. The neural network processes temperature patterns to deduce equipment state, removing the need for complex communication infrastructure while maintaining control coordination.
2Measurement precision
If temperature monitoring and neural network analysis are implemented, then determination accuracy of equipment state is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by using a simplified neural network that processes only essential temperature patterns rather than comprehensive environmental data. The system monitors temperature at key intervals and uses selective pattern recognition to determine equipment state, reducing computational energy while maintaining sufficient determination accuracy.
Solution Approach 2:
The neural network is trained offline to recognize temperature patterns associated with equipment states, enabling the system to self-determine operational status without requiring continuous heavy computational resources. Once trained, the model efficiently processes incoming temperature data with minimal energy consumption.
Data Source
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AI summary
Method for configuring a computer (4) of a home automation installation (100) or of a home automation sensor or of a home automation device (3) or of a management system (2), the computer incorporating a module (43) of artificial neural network logic, the method comprising: - a step of operating the neural network fed by temperature data, - a step of operating a reference system giving an operating state of a heating and/or air conditioning system (110), and - a step of modifying operating parameters of the neural network according to the operating state of the heating and/or air conditioning system (110) given by the reference system.